Gallery of Graphs

The plotting package matplotlib allows you to make very fancy graphs. We will discuss some of them in this lesson.

Pie Charts#

A pie chart uses pie slices to show the percentage of each constituent in the whole unit. It is commonly used in scientific publications to represent data. Let’s look at its implementation using matplotlib:

The pie chart is created using the plt.pie() command. There are two main arguments of plt.pie(). First is the size of each section, which is stored in amount. Second is their corresponding labels, which is stored in genre. These are plotted sequentially in the counterclockwise direction.

The autopct = '%1.1f%%' command shows the percentages to one decimal place. To format the percentage to the two decimal places, use autopct = '%1.2f%%', for three decimal places, use autopct = '%1.3f%%' and so on.

You may want to use plt.axis('equal') to make the scales along the horizontal and vertical axes equal so that the pie actually looks like a circle rather than an ellipse. We will use the colors keyword in your pie chart to specify a sequence of colors. The sequence must be between square brackets, each color must be between quotes preserving upper and lower cases, and they must be separated by commas. The sequence will be repeated if it is not long enough.

Use the help('matplotlib.pyplot.pie') command to explore more properties.

Histograms#

Histograms are another widely used 2-D graphs. Let’s see its basic implementation:

We have generated an array of random numbers using NumPy’s random library. Using the randn function, we are ensuring that the array has a standard normal distribution. We then plot this array using the axes.hist() function with the data serving as the input argument.

Use the help('matplotlib.pyplot.hist') command to explore more properties.

Bar graphs#

Bar graphs are easy to read representations of discrete data and matplotlib is used to implement it in Python. Let’s see an example of it below:

In line 4, we have used the randint from np.random class to generate 10 random integers between 1 and 100. Then we generated an array of the first 10 integers. We then plotted them using the axes.bar() function and set the ticks for the x-axis accordingly using the set_xticks method.

Use the help('matplotlib.pyplot.bar') command to explore more properties.

Box plot#

A boxplot presents information from a five-number summary. It does not show the distribution in as much detail but is useful for indicating whether a distribution is skewed and whether there are unusual observations in the data set. Boxplots are very useful when large numbers of observations are involved and two or more data sets are being compared.

The boxplot function creates the graph and returns a lot of stuff such as boxes. What you see in the graph is an orange line at the median of the data. The black box spans the IQR ranging from the lower quartile (25%) to the upper quartile (75%). The whiskers are the black lines that are connected to the 50% box with black lines. Let’s look at its implementation:

In lines 4 and 5, we have used randint from np.random class to generate two data sets of random integers named data1 and data2. We then store these data sets in the list data and plot it using the axes.boxplot command.

Use the help('matplotlib.pyplot.boxplot') command to explore more properties.

Polar plot#

In mathematics, the polar coordinate system is a two-dimensional coordinate system in which each point on a plane is determined by the distance from a reference point and the angle from a reference direction.

The reference point (analogous to the origin of a Cartesian system) is called the pole, and the ray from the pole in the reference direction is the polar axis. The distance from the pole is called the radial coordinate or radius, and the angle is called the angular coordinate, polar angle, or azimuth. Let’s look at its implementation in Python:

In the example above, r is the radial coordinate and theta is the polar coordinate. It is important to note that in line 7, we have created an axis and set the value of polar to True. This creates a polar axis and we have then plotted r and theta using the plot() function.

Use the help('matplotlib.pyplot.plot') command to explore more properties.

Fill between#

To color the area between curves or a curve and one of the axes, we use the fill_between function. Let’s see its implementation below:

Use the help('matplotlib.pyplot.fill_between') command to explore more properties.

Scatter plot#

To plot individual points without joining them, we can use scatter plots. There are two ways to make a scatter plot:

plt.plot(x, y, 's')

or

plt.scatter(x, y)

Use the help('matplotlib.pyplot.scatter') command to explore more properties.

We will learn about 3-D plots in the next lesson.